Low power device with contingent scheduling

ABSTRACT

Medical device systems and methods for operating medical device systems conserve energy by efficiently managing computational demands of the systems. A first analysis, having relatively lower computational processing demand than at least a second analysis, processes signals from a subject to determine a first estimate of a propensity for the subject to have a neurological event. If the first estimate meets a set of specified criteria, a second analysis is performed to determine a second estimate of the propensity for the subject to have a neurological event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/954,250 filed Nov. 30, 2015, which is a divisional of U.S. patentapplication Ser. No. 11/616,788 filed Dec. 27, 2006, the disclosures ofwhich are incorporated herein by reference in their entirety.

FIELD

The present invention relates generally to medical device systems.

BACKGROUND

A variety of medical device systems are used to measure physiologicalsignals from a subject and to process the signals and provideindications of potential or actual problem conditions. Computationaldemands of processing systems associated with the medical devicessystems produce drains on the power sources of these device systems andcan have a major impact on overall battery life. Moreover, in manymedical device systems, it is desirable to keep the system as small andunobtrusive as possible so that the patient can have it available at alltimes.

In the case of implantable systems, power source replacement may involvesurgery with its attendant costs and risks to the subject. Moreover,power source replacement may involve replacement of the implantablesystem itself because such units are typically hermetically sealed toreduce the likelihood of infection.

SUMMARY

The invention provides medical device systems and methods for operatingmedical device systems that provide energy savings by efficientlymanaging computational demands of the systems. Some such systemscomprise detectors to receive input from a subject, communicationssystems to deliver signals indicative of the input to a processor,processing systems to analyze the signals and determine one or moreconditions of the subject, and a communication system to provideindications of the one or more conditions to the subject.

The various embodiments describe ways for a medical device to provide atleast two stages of processing for signals measured from a subject. Afirst set of computer instructions, programmable logic, or circuitry,for example, one or more feature extractors and one or more classifiersprocess the signals to determine a first estimate of a susceptibility orpropensity of the subject to have a neurological event. If the firstestimate meets a set of criteria, a second set of computer instructions,programmable logic or circuitry, typically more computationallydemanding and/or more sensitive and possibly providing more specificresults than the first, is enabled to determine a second estimate of thepropensity for the subject to have a neurological event. At least one ofthe feature extractors may be selected based on information about thesubject. If desired, information related to the estimate may be outputto the subject. The systems and method can proceed in such a manner forany number of iterations. For example, a third set of computerinstructions, programmable logic or circuitry may be enabled if thesecond estimate meets a second set of criteria, and so forth.Information related to the estimate may be output to the subject. Thecomputer instructions, programmable logic or circuitry may be enabled ona processing system that is external to the subject, on a processingsystem that is implanted in the subject, or on combinations of externaland implanted processing systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating aspects of contingent use offeature extractors and classifiers;

FIG. 2 is an example of a classifier output;

FIG. 3 is a flow chart of an embodiment of contingent analysis;

FIG. 4 is an example timeline for an embodiment of an analysisscheduling that varies temporally.

FIG. 5 is a simplified diagram of a system that embodies a contingentscheduling system;

FIG. 6 is a block diagram of an implanted communications unit that maybe used in accordance with the systems and methods described herein;

FIG. 7 is a block diagram of an external data device that may be used inaccordance with the systems and methods described herein;

FIG. 8 is an example timeline for a typical therapeutic regimen for thetreatment of epilepsy; and

FIG. 9 is an example timeline for a therapeutic regimen for thetreatment of epilepsy that may be enabled by the system and methodsdescribed herein.

DETAILED DESCRIPTION

Certain specific details are set forth in the following description andfigures to provide an understanding of various embodiments of theinvention. Certain well-known details, associated electronics andmedical devices are not set forth in the following disclosure to avoidunnecessarily obscuring the various embodiments of the invention.Further, those of ordinary skill in the relevant art will understandthat they can practice other embodiments of the invention without one ormore of the details described below. Finally, while various processesare described with reference to steps and sequences in the followingdisclosure, the description is for providing a clear implementation ofparticular embodiments of the invention, and the steps and sequences ofsteps should not be taken as required to practice this invention.

As described in the background, a variety of medical device systems areused to measure physiological signals from a subject and to processthose signals. Some such medical device systems, especially thosecomprising ambulatory and implantable devices, operate on portable powersources such as batteries. Computational processing demands, especiallyin the case of substantially continuous or repeated monitoring of thesubject, can cause energy drains and may have a negative impact onbattery life. The various embodiments of the systems and methodsprovided herein reduce the computational burden on such systems toextend the battery life.

Although some of the discussion below focuses on measuringelectroencephalogram (“EEG”) signals of subjects and subject populationsfor the detection and prediction of epileptic seizures, it should beappreciated that the invention is not limited to measuring EEG signalsor to predicting epileptic seizures. For example, the invention couldalso be used in systems that measure one or more of a blood pressure,pulse oximetry, temperature of the brain or of portions of the subject,blood flow measurements, ECG/electrocardiogram (“EKG”), heart ratesignals, respiratory signals, chemical concentrations ofneurotransmitters, chemical concentrations of medications, pH in theblood, or other physiological or biochemical parameters of a subject.

Furthermore, aspects of the invention may be useful for monitoring andassisting in the treatments for a variety of conditions such as sleepapnea and other sleep disorders, migraine headaches, depression,Alzheimer's, Parkinson's Disease, dementia, attention deficit disorder,stroke, eating disorders, other neurological or psychiatric disorders,cardiac disease, diabetes, cancer, or the like.

Using epilepsy as an illustrative example, epilepsy is a disorder of thebrain characterized by neurological events in the form of chronic,recurring seizures and affects an estimated 50 million people worldwide.Seizures are a result of uncontrolled discharges of electrical activityin the brain. A seizure typically manifests itself as suddeninvoluntary, disruptive, and often destructive sensory, motor, andcognitive phenomena. Epilepsy is usually treated, though not cured, withmedication. Surgery may be indicated in cases in which seizure focus isidentifiable, and the seizure focus is not located in the eloquentcortex.

A single neurological event most often does not cause significantmorbidity or mortality, but severe or recurring neurological events canresult in major medical, social, and economic consequences. Epilepsy ismore often diagnosed in children and young adults. People withuncontrolled epilepsy are often significantly limited in their abilityto work in many industries and may not be able to legally drive anautomobile.

The cause of epilepsy is often uncertain. Symptomatic epilepsies arisedue to some structural or metabolic abnormality of the brain and mayresult from a wide variety of causes including genetic conditions,stroke, head trauma, complications during pregnancy or birth, infectionssuch as bacterial or viral encephalitis, or parasites. Idiopathicepilepsies are those for which no other condition has been implicated asa cause and are often genetic and generalized. In the majority of cases,the cause of a subject's epilepsy is unknown.

One of the most disabling aspects of neurological disorders such asepilepsy is the seeming unpredictability of neurological events such asseizures. Mechanisms underlying the generation of seizures are thoughtto operate over a period of seconds to minutes before the clinical onsetof a seizure. Typically, electrographic manifestations of a neurologicalevent are detectible some time before clinical manifestations occur.Most work in the quantitative analysis of neurological events has beenaimed at detecting these electrographic manifestations. NeuroPace, Inc.has been developing systems to detect the electrographic onset of aneurological event so that some action, such as direct electricalstimulation of certain brain structures, may be taken in an attempt topreempt the clinical onset of a neurological event. However, thedetection of the electrographic onset of a neurological event may notcome far enough in advance of the clinical onset for electricalstimulation or other therapies, such as the administration ofanticonvulsant drugs, to be effective in preventing the clinical onset.Additionally, seizure activity may already be causing harm to the brainbefore the clinical onset of the seizure.

It is desirable to be able to predict neurological events well beforetheir electrographic onset. Embodiments of predictive systems generallycomprise a collection of detectors for acquiring data from a subject andan analysis system for processing the data. Predictive analysis systemsare routinely considered to be comprised of arrangements of featureextractors and classifiers. Feature extractors are used to quantify orcharacterize certain aspects of the measured input signals. Classifiersare then used to combine the results obtained from the featureextractors into an overall answer or result. Systems may be designed todetect different types of conditions that may be reflective of neuralcondition. These could include, but are not limited, to systems designedto detect if the subject's neural condition is indicative of anincreased susceptibility or propensity for a neurological event orsystems designed to detect deviation from a normal condition. As can beappreciated, for other neurological or non-neurological disorders, theclassification of the subject's neural condition will be based onsystems, feature extractors and classifiers that are deemed to berelevant to the particular disorder.

FIG. 1 depicts an example of the overall structure of a system forestimating a propensity for the onset of a neurological event such as,for example, an epileptic seizure. The input data 102 may compriserepresentations of physiological signals obtained from monitoring asubject. Any number of signal channels may be used. Examples ofphysiological signals that may be used as input data 102 include, butare not limited to, electrical signals generated by electrodes placed onor within the brain or nervous system (EEG signals), temperature of thebrain or of portions of the brain, blood pressure or blood flowmeasurements, pulse oximetry, ECG/EKG, blood pH, chemical concentrationsof neurotransmitters, chemical concentrations of medications,combinations of the preceding, and the like.

The input data may be in the form of analog signal data or digitalsignal data that has been converted by way of an analog to digitalconverter (not shown). The signals may also be amplified, preprocessed,and/or conditioned to filter out spurious signals or noise. For purposesof simplicity the input data of all of the preceding forms is referredto herein as input data 102.

The input data 102 from the selected physiological signals is suppliedto one or more feature extractors 104 a, 104 b, 105. A feature extractor104 a, 104 b, 105 may be, for example, a set of computer executableinstructions stored on a computer readable medium, or a correspondinginstantiated object or process that executes on a computing device.Certain feature extractors may also be implemented as programmable logicor as circuitry. In general, a feature extractor 104 a, 104 b, 105 canprocess data 102 and identify some characteristic of the data 102. Sucha characteristic of the data is referred to herein as an extractedfeature.

Operation of a feature extractor 104 a, 104 b, 105 requires expenditureof electrical energy to process data and identify characteristics. Theamount of electrical energy required may depend on the complexity,quantity, and quality of the input data and on the complexity of theprocessing system applied to the input data. Feature extractors 104 a,104 b, 105 that are more complex generally require correspondinglylarger amounts of electrical energy. As described more fully below, insome embodiments, some feature extractors 105 may be optionally appliedor omitted in various circumstances. For example, when the applicationof one set of feature extractors 104 a, 104 b is sufficient to estimatethat a propensity for a neurological event is sufficiently low, thenother feature extractors 105 may not be applied to the input data 102.If the set of feature extractors 104 a, 104 b indicates a higherpropensity for a neurological event, then additional feature extractors105 may be applied to the input data 102.

Each feature extractor 104 a, 104 b, 105 may be univariate (operating ona single input data channel), bivariate (operating on two datachannels), or multivariate (operating on multiple data channels). Someexamples of potentially useful characteristics to extract from signalsfor use in determining the subject's propensity for a neurologicalevent, include but are not limited to, alpha band power (8-13 Hz), betaband power (13-18 Hz), delta band power (0.1-4 Hz), theta band power(4-8 Hz), low beta band power (12-15 Hz), mid-beta band power (15-18Hz), high beta band power (18-30 Hz), gamma band power (30-48 Hz),second, third and fourth (and higher) statistical moments of the EEGamplitudes, spectral edge frequency, decorrelation time, Hjorth mobility(HM), Hjorth complexity (HC), the largest Lyapunov exponent L(max),effective correlation dimension, local flow, entropy, loss of recurrenceLR as a measure of non-stationarity, mean phase coherence, conditionalprobability, brain dynamics (synchronization or desynchronization ofneural activity, STLmax, T-index, angular frequency, and entropy), linelength calculations, area under the curve, first, second and higherderivatives, integrals, or a combination thereof. Of course, for otherneurological conditions, additional or alternative characteristicextractors may be used with the systems described herein.

The extracted characteristics can be supplied to one or more classifiers106, 107. Like the feature extractors 104 a, 104 b, 105, each classifier106, 107 may be, for example, a set of computer executable instructionsstored on a computer readable medium or a corresponding instantiatedobject or process that executes on a computing device. Certainclassifiers may also be implemented as programmable logic or ascircuitry. Operation of a classifier 106, 107 requires electricalenergy. The classifiers can vary in complexity. Classifiers 106, 107that are more complex may require correspondingly larger amounts ofelectrical energy. In some embodiments, some classifiers may beoptionally applied or omitted in various circumstances. For example,when the application of one or more classifiers 106 is sufficient toestimate that a propensity for a neurological event is sufficiently low,then other classifiers 107 may not be applied to the extractedcharacteristics. If the classifiers 106 indicate a higher propensity fora neurological event, then additional classifiers 107 may be applied tothe extracted characteristics.

The classifiers 106, 107 analyze one or more of the extractedcharacteristics and possibly other subject dependent parameters toprovide a result 108 that may characterize, for example, a subject'sneural condition. Some examples of classifiers include k-nearestneighbor (“KNN”), neural networks, and support vector machines (“SVM”).Each classifier 106, 107 may provide a variety of output results, suchas a logical result or a weighted result. The classifiers 106, 107 maybe customized for the individual subject and may be adapted to use onlya subset of the characteristics that are most useful for the specificsubject. For example, the classifier may detect pre-onsetcharacteristics of a neurological event. Additionally, over time, theclassifiers 106, 107 may be further adapted to the subject, based, forexample, in part on the result of previous analyses and may reselectextracted characteristics that are used for the specific subject.

As it relates to epilepsy, for example, one implementation of aclassification of neural conditions defined by the classifiers 106, 107may include (1) an inter-ictal condition (sometimes referred to as a“normal” condition), (2) a pre-ictal condition (sometimes referred to asan “abnormal” or “pre-seizure” condition), (3) an ictal condition(sometimes referred to as a “seizure” condition), and (4) a post-ictalcondition (sometimes referred to as a “post-seizure” condition). Inanother embodiment, it may be desirable to have the classifier classifythe subject as being in one of two conditions—a pre-ictal condition orinter-ictal condition—which could correspond, respectively, to either anelevated or high propensity for a future seizure or a low propensity fora future seizure.

As noted above, instead of providing a logical answer, it may bedesirable for a classifier 106, 107 to provide a weighted answer so asto further delineate within the pre-ictal condition to further allow thesystem to provide a more specific output communication for the subject.For example, instead of a simple logical answer (e.g., pre-ictal orinter-ictal) it may be desirable to provide a weighted output or otheroutput that quantifies the subject's propensity, probability, likelihoodand/or risk of a future neurological event using some predeterminedscale (e.g., scale of 1-10, with a “1” meaning “normal” and a “10”meaning a neurological event is imminent). For example, if it isdetermined that the subject has an increased propensity for aneurological event (e.g., subject has entered the pre-ictal condition),but the neurological event is likely to occur on a long time horizon,the output signal could be weighted to be reflective of the long timehorizon, e.g., an output of “5”. However, if the output indicates thatthe subject is pre-ictal and it is predicted that the neurological eventis imminent within the next 10 minutes, the output could be weighted tobe reflective of the shorter time horizon to the neurological event,e.g., an output of “9.” On the other hand, if the subject is normal, thesystem may provide an output of “1”.

Other implementations involve classifier 106 outputs expressing theinter-ictal and pre-ictal conditions as a continuum, with a scalar orvector of parameters describing the actual condition and its variations.FIG. 2 depicts an example of a graphical display of the output of oneembodiment of a classifier over a period of time. The output of theclassifier at any point in time is a vector of two estimatedprobabilities: an estimated probability 152 that the input data isindicative of an inter-ictal condition and an estimated probability 154that the input data is indicative of a pre-ictal condition. The sum ofthe two probabilities 152, 154, at any given time is one. The estimatedprobabilities 152, 154 are plotted over a period of time beginningapproximately 360 minutes before the onset of a neurological event attime zero, indicated by the vertical axis 150. In the example graph, theestimated probability 152 that the data is indicative of an inter-ictalstate was larger than the estimated probability 154 that the data wasindicative of a pre-ictal state, until approximately 80 minutes beforethe onset 150 of the neurological event. The estimated probability 152then dropped to very small values beginning approximately 50 minutesbefore the onset 150. The estimated probability 154 that the data isindicative of a pre-ictal state remained low until approximately 80minutes before the onset 150 of the neurological event at which time itbegan to trend upward rapidly.

As described above, the computational demands of the processing providedby feature extractors 104 a, 104 b, 105 and classification provided byclassifiers 106, 107 can be extensive. In the case of ambulatory systemssupplied by portable power sources, such as implanted batteries,supplying the energy required to meet the computational demands canseverely limit power source life. In some applications, physiologicalsignals may be measured and analyzed continuously or often over longperiods of time and the need to conserve energy may be particularlyacute.

FIG. 3 depicts a simplified block diagram of a method of operatingmedical devices for analyzing signals that provides energy savings byenabling different parts of an overall algorithm to process signals onlyas needed in order to reduce energy consumption and optimize systemperformance. One or more physiological signals from a subject aremeasured 110. In one embodiment, sixteen channels of physiologicalsignals are measured. More or fewer channels may be measured accordingto the particular kinds of analyses being employed. The measured signalsmay be pre-processed, such as, for example, by amplification, filtering,and/or conversion from analog to digital, to generate input data 112 forthe analysis. Input data 112 may also comprise other subject dependentparameters (such as subject inputs and/or subject history data) that maybe indicative and/or predictive of a subject's propensity for aneurological event.

Typically, the physiological signal(s) from the subject are measuredduring a sliding observation window or epoch. Characteristics of thesliding window may be adapted based on previous measurements andanalysis. In particular, the sliding windows may operate continuously,periodically during specified intervals, or during an adaptivelymodified schedule (for example, to customize it to the specificsubject's cycles). In some embodiments, adaptations to the slidingwindow can be made automatically by the system. In some embodiments,adaptations to the window may be made by a clinician. For example, if itis known that the subject is prone to have a neurological event in themorning, a clinician may program the system to continuously monitor thesubject during the morning hours, while only periodically monitoring thesubject during the remainder of the day. As another example, it may beless desirable to monitor a subject and provide an output to a subjectwhen the subject is asleep. In such cases, the system may be programmedto discontinue monitoring or change the monitoring and communicationprotocol with the subject during a specified “sleep time” or whenever asubject inputs into the system that the subject is asleep or when thesystem determines that the subject is asleep. This could includeintermittent monitoring, monitoring with a varying duty cycle,decreasing of the sampling frequency, or other energy saving or dataminimization strategy during a time period in which the risk for aneurological event is low. Additionally, the system could enter into alow risk mode for a time period following each medication dose.

Input data 112 is subjected to a first stage analysis 114. The firststage analysis 114 may be performed by logic embodied in, for example,computer-executable instructions, such as program modules, executed byone or more computers or other devices. Generally, program modulesinclude routines, programs, objects, components, data structures, andthe like that perform particular tasks or implement particular abstractdata types. Typically the functionality of the program modules may becombined or distributed as desired in various embodiments. The firststage analysis 114 may comprise the application of one or more featureextractors and a classifier such as described above. Typically, thefirst stage analysis 114 will comprise the application of a subset ofavailable feature extractors applied to the input data to identify somecharacteristics of the signals. Preferably, the first stage analysiswill be relatively low in computational demands and will have arelatively high sensitivity, but not necessarily a high specificity. Ingeneral terms, the sensitivity of the analysis is related to theprobability that analysis indicates the presence of a condition giventhat the condition actually exists. In general terms, the specificity ofthe analysis is related to the probability that the analysis indicatesthe absence of a condition given that the condition is actually absent.The detection of particular frequencies in EEG signals is one example offeature extractor having a relatively low computational demand. Theoutput(s) from the first set of feature extractors may be combined usinga first classifier.

Based on the first stage analysis 114, a first estimate of asusceptibility or propensity for the subject to have a neurologicalevent is determined 116. The first estimate may take the form of aqualitative characterization or may be represented quantitatively or bya combination of qualitative and quantitative characterizations. Aqualitative characterization may, for example, relate to the presence ofpre-onset characteristics for a neurological event. A quantitativecharacterization may be a single number, such as, for example, by aprobability of a neurological event occurring in a predetermined timeperiod following the measurement of the signals or an estimated timehorizon during which an estimated propensity for the subject to have aneurological event is below a predetermined threshold, or a collectionof values that characterize the analysis.

The first estimate 116 is then examined 118 to determine whether itmeets one or more specified criteria. The specified criteria may beuniversal or may be adapted to a particular subject. By way of examples,the criteria may include the presence or absence of certain features inthe signals or the exceedance of a threshold probability. The criteriamay be modified over time. The criteria may be adapted in response tovarious conditions of the subject such as, for example, the subject'sstate of wakefulness or current activity level. The criteria may alsoadapted in response to current conditions of the medical device such as,for example, the current charge state of a battery.

If the criteria are not met, a second stage of analysis 120 is notperformed and the system may return to a monitoring condition 126. Inthis instance, the computational and energy costs of the second stageanalysis 120 are not incurred. For additional energy savings, forexample if it is determined that a seizure is very unlikely, the systemmay also reduce the sampling rate or cease monitoring and turn off or goto sleep for some specified amount of time. Such embodiments will dependon the predictive value of collecting continuous monitoring data. Forexample, if it can be determined that the value of such data is low,turning off may be a viable option for some amount of time.

If the criteria in step 118 are met, for example if the estimate derivedfrom the first analysis indicates an increased susceptibility orpropensity for the monitored condition to exist or occur (for example,prediction of the pre-ictal condition), then the algorithm maytransition from the base mode to a second or advanced mode wherein asecond stage analysis 120 is performed to determine a second estimate ofa propensity for the subject to have a neurological event 122. Thesecond stage analysis 120 may be performed by logic embodied in, forexample, computer-executable instructions, such as program modules,executed by one or more computers or other devices.

Depending on the particular embodiment, the set of feature extractorsemployed in the second stage analysis 120 may be used in conjunctionwith the set of feature extractors employed in the first stage analysis114 or as an alternative to the first set of feature extractors. The setof feature extractors employed in the second stage analysis 120 willtypically afford a higher level of computational complexity and/or mayhave a higher specificity and/or sensitivity than the set of featureextractors employed in the first stage analysis 114. In one embodiment,the second stage analysis 120 may perform more refined versions of theanalyses performed by the first stage analysis 114. In anotherembodiment, the second stage analysis 120 may perform different kinds ofanalyses. In yet other embodiments, the feature extractors in the firststage analysis and second stage analysis may have multi-resolutionpredictions and may provide for divergent spatial predictions. Forexample, the first stage analysis may include feature extractors thatmore accurately predict over a long time horizon, while the second stageanalysis may more accurately predict over a short time horizon.

The output from the second set of feature extractors may be combined inthe classifier used in the first stage analysis 114 or a secondclassifier. The result from either classifier may, in one embodiment,have both an improved sensitivity and specificity, relative to thesensitivity and specificity of the classification based on only thefirst set of feature extractors. The second estimate 122 is preferablymore refined than the first estimate 116 and may take the form of aqualitative or a quantitative characterization or a combination thereof.It will be appreciated, however, that battery life is saved regardlessof whether more or less computation is required to produce the secondestimate 122 than the first estimate 116.

Once the subject's susceptibility or propensity for seizure is estimatedby the predictive algorithm, a signal that is indicative of thepropensity for the future seizure may optionally be communicated to thesubject 124. In some embodiments, the predictive algorithm provides anoutput that indicates when the subject has an elevated propensity forseizure. In such embodiments, the communication output to the subjectmay simply be a warning or a recommendation to the subject that wasprogrammed into the system by the clinician. In other embodiments, thepredictive algorithm may output a graded propensity assessment, aquantitative assessment of the subject's condition, a time horizon untilthe predicted seizure will occur, or some combination thereof. In suchembodiments, the communication output to the subject may provide arecommendation or instruction that is a function of the risk assessment,probability, or time horizon.

It will be recognized by those skilled in the art that the methoddescribed herein can readily be extended to encompass more than twostages of analysis. In one embodiment, the result of the first stageanalysis may determine which of a plurality of second stage algorithmsis selected to run. In another embodiment, the result of a second stageanalysis may be used to decide whether a third stage of analysis is run,which result may trigger a fourth stage analysis, and so on.

In alternative embodiments of the energy saving methods disclosedherein, the predictive algorithms are run less often when previousresults indicate that it is unlikely that a neurological event isimminent. For example, if the result of a previous execution of thealgorithm indicates that it is relatively more likely that aneurological event will occur within a given time interval, thepredictive algorithm may be scheduled to execute more frequently. Suchvariable scheduling techniques may be usefully combined with the otherscheduling techniques discussed in detail herein.

FIG. 4 depicts an illustrative example of one such alternativeembodiment. Each bar represents a result of the predictive system asmeasuring and analysis cycles were run at various times, t₁, t₂, etc. Attime t₁, a relatively low propensity of a neurological event wasestimated and the next running of the analysis was scheduled for timet₂. A similarly low propensity was estimated at time t₂, and so thesystem was scheduled to be run again at time t₃, where the time intervalt₃−t₂ is the same as the time interval t₂−t₁. At time t₄, an elevatedpropensity was estimated and so the analysis system was scheduled toexecute more frequently, with a shorter interval between succeedingexecutions. The estimated propensity did not change again until thesystem was executed at time t₈, at which time the estimated propensitywas once again lower and so the system was scheduled to run on thebaseline schedule as it had been at t₁. At t₁₁, a highly elevatedpropensity was estimated, and so the system was scheduled to run on amuch more frequent schedule which continued until a lowered propensitywas estimated at time t₁₆.

The length and frequency of measurement and analysis cycles may betailored to the prediction horizon. As an example, if the predictivesystem indicates that it is unlikely for a neurological event to occurin the next hour, the system could be scheduled to run more often thanonce per hour, but not so often as several hundred times per hour.Preferred scheduling frequencies would be between about 2 and about 100times the reciprocal of the system's neurological event predictionhorizon. By varying the frequency of measuring and analysis cyclesaccording to the estimated propensity, energy savings would be realized.

In an alternative embodiment, different prediction subsystems may be rundepending on the results of prior calculations. For example, if thecurrent propensity for a neurological event is remote, a correspondingexceptionally low operating power analysis subsystem would be scheduled.If the low operating power analysis subsystem indicates an elevatedpropensity for a neurological event, a second, more computationallydemanding and more specific subsystem would be scheduled. If the outputof the second subsystem indicates a propensity above a certainthreshold, a third subsystem may be scheduled, and so forth. In yetanother alternative embodiment, the approaches of the two precedingembodiments may be combined. The selection of subsystems and their ratesof execution may depend on the results of prior analyses.

The systems described herein may be embodied as software, hardware,firmware, or combinations thereof. In some instances, it may bedesirable to have first or lower stage systems operating only inhardware in order to minimize energy requirements. The systems describedabove may be embodied in a device external to the subject, an implanteddevice, or distributed between an implanted device and an externaldevice.

Because the methods of the present invention are able to consume lessenergy than conventional algorithms and will thereby prolong the life ofthe power sources, the methods of the present invention will facilitatethe long-term implementation of the algorithms in a portable and/orimplantable device system. FIG. 5 illustrates a system in which thealgorithms of the present invention may be embodied. The system 200 isused to monitor a subject 202 for purposes of detecting and predictingneurological events. The system 200 of the embodiment provides forsubstantially continuous sampling of brain wave electrical signals suchas in electroencephalograms or electrocorticograms, referred tocollectively as EEGs.

The system 200 comprises one or more sensors 204 configured to measuresignals from the subject 202. The sensors 204 may be located anywhere onthe subject. In the exemplary embodiment, the sensors 204 are configuredto sample electrical activity from the subject's brain, such as EEGsignals. The sensors 204 may be attached to the surface of the subject'sbody (e.g., scalp electrodes), attached to the head (e.g., subcutaneouselectrodes, bone screw electrodes, and the like), or, preferably, may beimplanted intracranially in the subject 202. In one embodiment, one ormore of the sensors 204 will be implanted adjacent a previouslyidentified epileptic focus, a portion of the brain where such a focus isbelieved to be located, or adjacent a portion of a seizure network.

Any number of sensors 204 may be employed, but the sensors 204 willpreferably include between 1 sensor and 16 sensors. The sensors may takea variety of forms. In one embodiment, the sensors comprise gridelectrodes, strip electrodes and/or depth electrodes which may bepermanently implanted through burr holes in the head. Exact positioningof the sensors will usually depend on the desired type of measurement.In addition to measuring brain activity, other sensors (not shown) maybe employed to measure other physiological signals from the subject 202.

In an embodiment, the sensors 204 will be configured to substantiallycontinuously sample the brain activity of the groups of neurons in theimmediate vicinity of the sensors 204. The sensors 204 are electricallyjoined via cables 206 to an implanted communication unit 208. In oneembodiment, the cables 206 and communication unit 208 will be implantedin the subject 202. For example, the communication unit 208 may beimplanted in a subclavicular cavity of the subject 202. In alternativeembodiments, the cables 206 and communication unit 208 may be attachedto the subject 202 externally.

In one embodiment, the communication unit 208 is configured tofacilitate the sampling of signals from the sensors 204. Sampling ofbrain activity is typically carried out at a rate above about 200 Hz,and preferably between about 200 Hz and about 1000 Hz, and mostpreferably at about 400 Hz. The sampling rates could be higher or lower,depending on the specific conditions being monitored, the subject 202,and other factors. Each sample of the subject's brain activity istypically encoded using between about 8 bits per sample and about 32bits per sample, and preferably about 16 bits per sample.

In alternative embodiments, the communication unit 208 may be configuredto measure the signals on a non-continuous basis. In such embodiments,signals may be measured periodically or aperiodically.

An external data device 210 is preferably carried external to the bodyof the subject 202. The external data device 210 receives and storessignals, including measured signals and possibly other physiologicalsignals, from the communication unit 208. External data device 210 couldalso receive and store extracted features, classifier outputs, patientinputs, and the like. Communication between the external data device 210and the communication unit 208 may be carried out through wirelesscommunication. The wireless communication link between the external datadevice 210 and the communication unit 208 may provide a one-way ortwo-way communication link for transmitting data. In alternativeembodiments, it may be desirable to have a direct communications linkfrom the external data device 210 to the communication unit 208, suchas, for example, via an interface device positioned below the subject'sskin. The interface (not shown) may take the form of a magneticallyattached transducer that would enable power to be continuously deliveredto the communication unit 208 and would provide for relatively higherrates of data transmission. Error detection and correction methods maybe used to help insure the integrity of transmitted data. If desired,the wireless data signals can be encrypted prior to transmission to theexternal data device 210.

FIG. 6 depicts a block diagram of one embodiment of a communication unit208 that may be used with the systems and methods described herein.Energy for the system is supplied by a rechargeable power supply 224.The rechargeable power supply may be a battery, or the like. Therechargeable power supply 224 may also be in communication with atransmit/receive subsystem 226 so as to receive power from outside thebody by inductive coupling, radiofrequency (RF) coupling, and the like.Power supply 224 will generally be used to provide power to the othercomponents of the implantable device. Signals 212 from the sensors 204are received by the communication unit 208. The signals may be initiallyconditioned by an amplifier 214, a filter 216, and an analog-to-digitalconverter 218. A memory module 220 may be provided for storage of someof the sampled signals prior to transmission via a transmit/receivesubsystem 226 and antenna 228 to the external data device 210. Forexample, the memory module 220 may be used as a buffer to temporarilystore the conditioned signals from the sensors 204 if there are problemswith transmitting data to the external data device 210, such as mayoccur if the external data device 210 experiences power problems or isout of range of the communications system. The external data device 210can be configured to communicate a warning signal to the subject in thecase of data transmission problems to inform the subject and allow himor her to correct the problem.

The communication unit 208 may optionally comprise circuitry of adigital or analog or combined digital/analog nature and/or amicroprocessor, referred to herein collectively as “microprocessor” 222,for processing the signals prior to transmission to the external datadevice 210. The microprocessor 222 may execute at least portions of theanalysis as described herein. For example, in some configurations, themicroprocessor 222 may run one or more feature extractors 104 a, 104 b,105 (FIG. 1 ) that extract characteristics of the measured signal thatare relevant to the purpose of monitoring. Thus, if the system is beingused for diagnosing or monitoring epileptic subjects, the extractedcharacteristics (either alone or in combination with othercharacteristics) may be indicative or predictive of a neurologicalevent. Once the characteristic(s) are extracted, the microprocessor 222may transmit the extracted characteristic(s) to the external data device210 and/or store the extracted characteristic(s) in memory 220. Becausethe transmission of the extracted characteristics is likely to includeless data than the measured signal itself, such a configuration willlikely reduce the bandwidth requirements for the communication linkbetween the communication unit 208 and the external data device 210.

In some configurations, the microprocessor 222 in the communication unit208 may run one or more classifiers 106, 107 (FIG. 1 ) as describedabove with respect to FIG. 1 . The result 108 (FIG. 1 ) of theclassification may be communicated to the external data device 210.

While the external data device 210 may include any combination ofconventional components, FIG. 7 provides a schematic diagram of some ofthe components that may be included. Signals from the communication unit208 are received at an antenna 230 and conveyed to a transmit/receivesubsystem 232. The signals received may include, for example, a rawmeasured signal, a processed measured signal, extracted characteristicsfrom the measured signal, a result from analysis software that ran onthe implanted microprocessor 222, or any combination thereof.

The received data may thereafter be stored in memory 234, such as a harddrive, random access memory (“RAM”), electrically erasable programmableread-only memory (“EEPROM”), removable flash memory, or the like and/orprocessed by a microprocessor, application specific integrated circuit(ASIC) or other dedicated circuitry of a digital or analog or combineddigital/analog nature, referred to herein collectively as a“microprocessor” 236. Microprocessor 236 may be configured to requestthat the communication unit 208 perform various checks (e.g., sensorimpedance checks) or calibrations prior to signal recording and/or atspecified times to ensure the proper functioning of the system.

Data may be transmitted from memory 234 to microprocessor 236 where thedata may optionally undergo additional processing. For example, if thetransmitted data is encrypted, it may be decrypted. The microprocessor236 may also comprise one or more filters that filter out low-frequencyor high-frequency artifacts (e.g., muscle movement artifacts, eye-blinkartifacts, chewing, and the like) so as to prevent contamination of themeasured signals.

External data device 210 will typically include a user interface 240 fordisplaying outputs to the subject and for receiving inputs from thesubject. The user interface will typically comprise outputs such asauditory devices (e.g., speakers) visual devices (e.g., liquid-crystaldisplay (“LCD”), light-emitting diodes (“LEDs”)), tactile devices (e.g.,vibratory mechanisms), or the like, and inputs, such as a plurality ofbuttons, a touch screen, and/or a scroll wheel.

The user interface may be adapted to allow the subject to indicate andrecord certain events. For example, the subject may indicate thatmedication has been taken, the dosage, the type of medication, mealintake, sleep, drowsiness, occurrence of an aura, occurrence of aneurological event, or the like. Such inputs may be used in conjunctionwith the measured data to improve the analysis.

The LCD display may be used to output a variety of differentcommunications to the subject including, status of the device (e.g.,memory capacity remaining), battery state of one or more components ofsystem, whether or not the external data device 210 is withincommunication range of the communication unit 208, a warning (e.g., aneurological event warning), a prediction (e.g., a neurological eventprediction), a recommendation (e.g., “take medicine”), or the like. Itmay be desirable to provide an audio output or vibratory output to thesubject in addition to or as an alternative to the visual display on theLCD.

External data device 210 may also include a power source 242 or otherconventional power supply that is in communication with at least oneother component of external data device 210. The power source 242 may berechargeable. If the power source 242 is rechargeable, the power sourcemay optionally have an interface for communication with a charger 244.External data device 210 may also include a USB interface 246 configuredto communicate with a computer 238. While not shown in FIG. 7 , externaldata device 210 will typically comprise a clock circuit (e.g.,oscillator and frequency synthesizer) to provide the time base forsynchronizing the external data device 210 and the communication unit208.

Referring again to FIG. 5 , in a preferred embodiment, most or all ofthe processing of the signals received by the communication unit 208 isdone in an external data device 210 that is external to the subject'sbody. In such embodiments, the communication unit 208 would receive thesignals from subject and may or may not pre-process the signals andtransmit some or all of the measured signals transcutaneously to anexternal data device 210, where the prediction of the neurological eventand possible therapy determination is made. Advantageously, suchembodiments reduce the amount of computational processing power thatneeds to be implanted in the subject, thus potentially reducing energyconsumption and increasing battery life. Furthermore, by having theprocessing external to the subject, the judgment or decision makingcomponents of the system may be more easily reprogrammed or customtailored to the subject without having to reprogram the communicationunit 208.

In alternative embodiments, the predictive systems disclosed herein andtreatment systems responsive to the predictive systems may be embodiedin a device that is implanted in the subject's body, external to thesubject's body, or a combination thereof. For example, in one embodimentthe predictive system may be stored in and processed by thecommunication unit 208 that is implanted in the subject's body. Atreatment analysis system, in contrast, may be processed in a processorthat is embodied in an external data device 210 external to thesubject's body. In such embodiments, the subject's propensity forneurological event characterization (or whatever output is generated bythe predictive system that is predictive of the onset of theneurological event) is transmitted to the external subject communicationassembly, and the external processor performs any remaining processingto generate and display the output from the predictive system andcommunicate this to the subject. Such embodiments have the benefit ofsharing processing power, while reducing the communications demands onthe communication unit 208. Furthermore, because the treatment system isexternal to the subject, updating or reprogramming the treatment systemmay be carried out more easily.

In other embodiments, the signals 212 may be processed in a variety ofways in the communication unit 208 before transmitting data to theexternal data device 210 so as to reduce the total amount of data to betransmitted, thereby reducing the power demands of the transmit/receivesubsystem 226. Examples include: digitally compressing the signalsbefore transmitting them; selecting only a subset of the measuredsignals for transmission; selecting a limited segment of time andtransmitting signals only from that time segment; extracting salientcharacteristics of the signals, transmitting data representative ofthose characteristics rather than the signals themselves, andtransmitting only the result of classification. Further processing andanalysis of the transmitted data may take place in the external datadevice 210.

In yet other embodiments, it may be possible to perform some of theprediction in the communication unit 208 and some of the prediction inthe external data device 210. For example, one or more characteristicsfrom the one or more signals may be extracted with feature extractors inthe communication unit 208. Some or all of the extracted characteristicsmay be transmitted to the external data device 210 where thecharacteristics may be classified to predict the onset of a neurologicalevent. If desired, external data device 210 may be customizable to theindividual subject. Consequently, the classifier may be adapted to allowfor transmission or receipt of only the characteristics from thecommunication unit 208 that are predictive for that individual subject.Advantageously, by performing feature extraction in the communicationunit 208 and classification in an external device at least two benefitsmay be realized. First, the amount of wireless data transmitted from thecommunication unit 208 to the external data device 210 is reduced(versus transmitting pre-processed data). Second, classification, whichembodies the decision or judgment component, may be easily reprogrammedor custom tailored to the subject without having to reprogram thecommunication unit 208.

In yet another embodiment, feature extraction may be performed externalto the body. Pre-processed signals (e.g., filtered, amplified, convertedto digital) may be transcutaneously transmitted from communication unit208 to the external data device 210 where one or more characteristicsare extracted from the one or more signals with feature extractors. Someor all of the extracted characteristics may be transcutaneouslytransmitted back into the communication unit 208, where a second stageof processing may be performed on the characteristics, such asclassifying of the characteristics (and other signals) to characterizethe subject's propensity for the onset of a future neurological event.If desired, to improve bandwidth, the classifier may be adapted to allowfor transmission or receipt of only the characteristics from the subjectcommunication assembly that are predictive for that individual subject.Advantageously, because feature extractors may be computationallyexpensive and energy hungry, it may be desirable to have the featureextractors external to the body, where it is easier to provide moreprocessing and larger power sources.

For additional energy savings, the systems of the present invention mayalso embody some of the energy saving concepts described in commonlyowned, patent application Ser. No. 11/616,793, entitled “Low PowerDevice with Variable Scheduling,” filed Dec. 27, 2006, pending, thecomplete disclosure of which is incorporated herein by reference.

More complete descriptions of systems that may embody the concepts ofthe present invention are described in commonly owned U.S. Pat. No.8,868,172, filed Dec. 28, 2005, U.S. Pat. No. 8,725,243, filed Dec. 28,2005, and U.S. patent application Ser. No. 11/322,150, filed on Dec. 28,2005, published as US 2007/0149952 A1, abandoned, the completedisclosures of which are incorporated herein by reference.

The inventive aspects described herein may be applicable to commercialmonitoring systems. For example, the systems herein may be applied tothe NeuroPace® RNS system. Such commercial systems extract half-waveamplitude and duration, sum of absolute differences as an approximationof signal curve length (which is in turn a simplification of waveformfractal dimension), and a modified sum of absolute amplitudes as anapproximation of signal energy. Instead of running all of the featureextractors continuously all of the time, subsequent measurement andanalysis cycles may be scheduled based on analysis of previousmeasurement cycles. In some embodiments, the measuring and analysiscycles are run less often when the previous measurements indicates thatit is relatively unlikely that a seizure is imminent. On the other hand,if previous measurements indicate that a seizure is relatively likely tobe proximate, the measurement and analysis cycles are run morefrequently, up to, possibly, some predetermined maximum rate. Such afeature extractor configuration will preserve computation power, reducebattery usage, and prolong the time between battery changes.

The ability to provide long-term low-power ambulatory measuring ofphysiological signals and prediction of neurological events canfacilitate improved treatment regimens for certain neurologicalconditions. FIG. 8 depicts the typical course of treatment for a subjectwith epilepsy. Because the occurrence of neurological events 300 overtime has been unpredictable, present medical therapy relies oncontinuous prophylactic administration of anti-epileptic drugs (“AEDs”).Constant doses 302 of one or more AEDs are administered to a subject atregular time intervals with the objective of maintaining relativelystable levels of the AEDs within the subject. Maximum doses of the AEDsare limited by the side effects of their chronic administration.

Reliable long-term essentially continuously operating neurological eventprediction systems would facilitate epilepsy treatment. Therapeuticactions, such as, for example, brain stimulation, peripheral nervestimulation (e.g., vagus nerve stimulation), cranial nerve stimulation(e.g., trigeminal nerve stimulation (“TNS”)), or targeted administrationof AEDs, could be directed by output from a neurological eventprediction system. One such course of treatment is depicted in FIG. 9 .Relatively lower constant doses 304 of one or more AEDs may beadministered to a subject at regular time intervals in addition to or asan alternative to the prophylactic administration of AEDs. Supplementarymedication doses 306 are administered just prior to an imminentneurological event 308. By targeting the supplementary doses 306 at theappropriate times, neurological events may be more effectivelycontrolled and potentially eliminated 308, while reducing side effectsattendant with the chronic administration of higher levels of the AEDs.

While the present disclosure has been described in connection withvarious embodiments, illustrated in the various figures, it isunderstood that similar aspects may be used or modifications andadditions may be made to the described aspects of the disclosedembodiments for performing the same function of the present disclosurewithout deviating therefrom. Other equivalent mechanisms to thedescribed aspects are also contemplated by the teachings herein.Therefore, the present disclosure should not be limited to any singleaspect, but rather construed in breadth and scope in accordance with theappended claims.

What is claimed:
 1. A method of operating a medical device configured tomonitor a subject, the method comprising: receiving, at a processor, afirst signal reflective of neurological activity of the subject from asensor communicating with the subject; performing, by the processor, afirst analysis of the first signal to estimate a first deviation of thesubject from a normal neurological condition, including analyzing thefirst signal by a first set of feature extractors, in response toreceiving the first signal; determining that the first deviation meetsone or more specified criteria; subsequent to the first deviationmeeting the one or more specified criteria, receiving, at the processor,a second signal reflective of neurological activity of the subject; andperforming, by the processor, a second analysis of the second signal toestimate a second deviation of the subject from the normal neurologicalcondition, including analyzing the second signal by a second set offeature extractors different from the first set of feature extractors,the first analysis requiring less computational power than the secondanalysis, in response to receiving the second signal.
 2. The method ofclaim 1, wherein receiving the first signal comprises receiving, at theprocessor set in a base mode, the first signal reflective ofneurological activity of the subject from the sensor implantedintracranially in the subject.
 3. The method of claim 1, whereinreceiving the first signal comprises receiving, at the processor set ina base mode, the first signal reflective of neurological activity of thesubject from the sensor implanted adjacent to an identified epilepticfocus.
 4. The method of claim 1, wherein the sensor comprises a gridelectrode, a strip electrode, or a depth electrode.
 5. The method ofclaim 1, wherein at least one of the one or more specified criteria iscustomized to the subject.
 6. The method of claim 1, further comprisingmodifying at least one of the one or more specified criteria based on aresult of at least one of the first analysis or the second analysis. 7.The method of claim 1, wherein receiving the first signal comprisesreceiving, at the processor set in a base mode, the first signalreflective of neurological activity of the subject from the sensorcommunicating with the subject according to a sliding observationwindow.
 8. The method of claim 7, wherein the sliding observation windowincludes a first time period during which the subject is at a higherrisk for a neurological event and a second time period during which thesubject is at a lower risk for the neurological event.
 9. The method ofclaim 8, wherein the first time period and the second time period arebased on at least one of a time of day or a status of the subject. 10.The method of claim 8, wherein receiving the first signal furthercomprises receiving, at the processor set in the base mode, the firstsignal reflective of neurological activity according to a firstmonitoring protocol during the first time period and according to asecond monitoring protocol during the second time period, wherein thesecond monitoring protocol is configured for at least one of energysaving or data minimization.
 11. The method of claim 1, wherein adeviation of the subject from the normal neurological conditioncorresponds to an adverse condition of the subject, the adversecondition being an episode of epilepsy, migraine, depression,Alzheimer's, Parkinson's Disease, dementia, attention deficit disorder,or an eating disorder.
 12. The method of claim 1, further comprisingtransitioning the processor from a base mode to an advanced mode inresponse to the first deviation meeting the one or more specifiedcriteria, wherein the second analysis is performed by the processor. 13.A system configured to monitor a subject, comprising: a processor; and anon-transitory computer-readable medium storing instructions that, whenexecuted by the processor, cause the processor to: receive a firstsignal reflective of neurological activity of the subject from a sensorcommunicating with the subject; perform a first analysis of the firstsignal to estimate a first deviation of the subject from a normalneurological condition, including analyzing the first signal by a firstset of feature extractors, in response to receiving the first signal;determine that the first deviation meets one or more specified criteria;receive a second signal reflective of neurological activity of thesubject subsequent in time to the first signal in response totransitioning the processor from a base mode to an advanced mode; andperform a second analysis of the second signal to estimate a seconddeviation of the subject from the normal neurological condition,including analyzing the second signal by a second set of featureextractors different from the first set of feature extractors, the firstanalysis requiring less computational power than the second analysis, inresponse to receiving the second signal.
 14. The system of claim 13,wherein the instructions cause the processor to receive the first signalreflective of neurological activity of the subject from the sensorimplanted intracranially in the subject.
 15. The system of claim 13,wherein the instructions cause the processor to receive the first signalreflective of neurological activity of the subject from the sensorimplanted adjacent to an identified epileptic focus.
 16. The system ofclaim 13, wherein at least one of the one or more specified criteria iscustomized to the subject.
 17. The system of claim 13, wherein theinstructions further cause the processor to modify at least one of theone or more specified criteria based on a result of at least one of thefirst analysis or the second analysis.
 18. The system of claim 13,wherein the instructions cause the processor to receive the first signalreflective of neurological activity of the subject from the sensorcommunicating with the subject according to a sliding observationwindow.
 19. The system of claim 18, wherein the instructions cause theprocessor to receive the first signal reflective of neurologicalactivity according to a first monitoring protocol during the first timeperiod and according to a second monitoring protocol during the secondtime period, wherein the second monitoring protocol is configured for atleast one of energy saving or data minimization.
 20. The system of claim13, wherein a deviation of the subject from the normal neurologicalcondition corresponds to an adverse condition of the subject, theadverse condition being an episode of epilepsy, migraine, depression,Alzheimer's, Parkinson's Disease, dementia, attention deficit disorder,or an eating disorder.